This article introduces the ScaleLong diffusion model proposed by Sun Yat-sen University and other teams, which improves training stability by scaling the long skip connection of UNet. The research team conducted an in-depth analysis of the principle of accelerated training by the 1/√2 scaling operation, and proposed two methods, LS and CS, to effectively alleviate the instability problem during model training. This research result is of significant significance in improving the stability of the diffusion model and provides important technical support for the practical application of the diffusion model.
Sun Yat-sen University and other teams proposed the ScaleLong diffusion model, which stabilizes model training by scaling the long skip connection of UNet. They analyzed the principle of 1/√2 scaling operation to accelerate training, and effectively alleviated the instability in model training through LS and CS methods. These simple and effective methods are of great significance to the stability of diffusion models.
The proposal of the ScaleLong model and its related methods has brought new breakthroughs to the training stability of the diffusion model, and provided valuable experience and direction for the development of more stable and efficient diffusion models in the future. Looking forward to seeing more research results based on this in the future.